import os
import glob
import numpy as np
import pandas as pd

import logging
logging.getLogger().setLevel(logging.CRITICAL)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

import warnings
warnings.filterwarnings('ignore')

from utilities import DataVisualizer, DataDiscretizer, DataProcessor, DataEncoder

# Define folder names
DATA_FOLDER_NAME = r".\data"


ORIGINAL_DATASETS_IMOLA_FOLDER_NAME   = os.path.join(DATA_FOLDER_NAME, "datasets-imola")
DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME   = os.path.join(DATA_FOLDER_NAME, "discretized-datasets-imola")

AGGREGATED_DATASETS_IMOLA_FOLDER_NAME = os.path.join(DATA_FOLDER_NAME, "aggregated-datasets-imola")
DISCRETIZED_AGGREGATED_DATASETS_IMOLA_FOLDER_NAME = os.path.join(DATA_FOLDER_NAME, "discretized-aggregated-datasets-imola")

# Get all CSV dataset files from the specified folder
datasets_imola = glob.glob(os.path.join(ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, "*.csv"))

print(f"📂 Found {len(datasets_imola)} datasets in '{ORIGINAL_DATASETS_IMOLA_FOLDER_NAME}'")

📂 Found 3 datasets in '.\data\datasets-imola'

# Select the dataset file to analyze
dataset_path = next((file for file in datasets_imola if '20241128-imola.csv' in file), None)

if dataset_path:
    print(f"📂 Loading dataset: {dataset_path}")

    # Load the dataset into a DataFrame
    imola_20241128_df = pd.read_csv(dataset_path)

    # Display basic dataset information
    print("\n🔍 Dataset Overview:")
    print(imola_20241128_df.info())

    print("\n📊 First 5 Rows:")
    display(imola_20241128_df.head())

    print("\n📈 Summary Statistics:")
    display(imola_20241128_df.describe())

    # Get distribution overview with resulting bins
    bins_result = DataVisualizer.plot_distributions_overview(
        df=imola_20241128_df, 
        columns=imola_20241128_df.columns.difference(['InverterFault']).tolist(), 
        bins='auto', 
        quantiles=[0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]
    )

    # Print bin suggestions in a formatted way
    print("\n📌 Bin Distributions info:")
    for column, stats in bins_result.items():
        print(f"\n🔹 {column}:")
        for key, value in stats.items():
            print(f"   - {key}: {value}")

else:
    print("⚠ Dataset '20241128-imola.csv' not found in the specified folder.")

📂 Loading dataset: .\data\datasets-imola\20241128-imola.csv

🔍 Dataset Overview:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 248448 entries, 0 to 248447
Data columns (total 14 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   BatteryVoltage_V             248448 non-null  float64
 1   BatteryCurrent_A             248448 non-null  float64
 2   BatteryPackTemp_C            248448 non-null  float64
 3   InverterFault                248448 non-null  int64  
 4   InverterSpeed_RearLeft_RPM   248448 non-null  float64
 5   Inverter_Iq_Ref_RearLeft_A   248448 non-null  float64
 6   Inverter_Id_Ref_RearLeft_A   248448 non-null  float64
 7   MotorTemp_RearLeft_C         248448 non-null  float64
 8   InverterTemp_RearLeft_C      248448 non-null  float64
 9   InverterSpeed_RearRight_RPM  248448 non-null  float64
 10  Inverter_Iq_Ref_RearRight_A  248448 non-null  float64
 11  Inverter_Id_Ref_RearRight_A  248448 non-null  float64
 12  MotorTemp_RearRight_C        248448 non-null  float64
 13  InverterTemp_RearRight_C     248448 non-null  float64
dtypes: float64(13), int64(1)
memory usage: 26.5 MB
None

📊 First 5 Rows:

📈 Summary Statistics:

📌 Bin Distributions info:

🔹 BatteryCurrent_A:
   - num_bins: 6
   - quantile_edges: [-36.68999862670898, -0.0900000035762786, 0.0599999986588954, 0.0900000035762786, 10.65999984741211, 34.4900016784668, 115.23999786376952]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 BatteryPackTemp_C:
   - num_bins: 6
   - quantile_edges: [13.75, 13.899999618530272, 16.75, 21.959999084472656, 27.81999969482422, 29.25, 29.68000030517578]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 BatteryVoltage_V:
   - num_bins: 6
   - quantile_edges: [413.5, 449.260009765625, 453.7999877929688, 468.5400085449219, 485.4200134277344, 495.6400146484375, 510.7799987792969]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterSpeed_RearLeft_RPM:
   - num_bins: 6
   - quantile_edges: [-76.0, -2.0, 0.0, 3.0, 6012.0, 7515.0, 14881.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterSpeed_RearRight_RPM:
   - num_bins: 6
   - quantile_edges: [-11.0, 0.0, 0.0, 0.0, 5861.0, 7448.0, 12655.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterTemp_RearLeft_C:
   - num_bins: 6
   - quantile_edges: [15.0, 24.0, 27.0, 30.0, 33.0, 35.0, 45.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterTemp_RearRight_C:
   - num_bins: 6
   - quantile_edges: [15.0, 24.0, 27.0, 30.0, 33.0, 35.0, 44.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Id_Ref_RearLeft_A:
   - num_bins: 6
   - quantile_edges: [-49.8125, -4.6875, -2.0625, 0.0, 0.0, 0.0, 0.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Id_Ref_RearRight_A:
   - num_bins: 6
   - quantile_edges: [-45.375, -4.3125, -2.0, 0.0, 0.0, 0.0, 0.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Iq_Ref_RearLeft_A:
   - num_bins: 6
   - quantile_edges: [-26.75, 0.0, 0.0, 0.0, 11.5625, 26.75, 52.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Iq_Ref_RearRight_A:
   - num_bins: 6
   - quantile_edges: [-50.5, -24.75, -10.8125, 0.0, 0.0, 0.125, 32.875]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 MotorTemp_RearLeft_C:
   - num_bins: 6
   - quantile_edges: [12.0, 16.0, 32.0, 39.0, 49.0, 61.0, 76.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 MotorTemp_RearRight_C:
   - num_bins: 6
   - quantile_edges: [13.0, 17.0, 34.0, 42.0, 54.0, 65.0, 80.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

# Select the dataset file to analyze
dataset_path = next((file for file in datasets_imola if '20250113-imola.csv' in file), None)

if dataset_path:
    print(f"📂 Loading dataset: {dataset_path}")

    # Load the dataset into a DataFrame
    imola_20250113_df = pd.read_csv(dataset_path)

    # Display basic dataset information
    print("\n🔍 Dataset Overview:")
    print(imola_20250113_df.info())

    print("\n📊 First 5 Rows:")
    display(imola_20250113_df.head())

    print("\n📈 Summary Statistics:")
    display(imola_20250113_df.describe())

    # Get distribution overview with resulting bins
    bins_result = DataVisualizer.plot_distributions_overview(
        df=imola_20250113_df, 
        columns=imola_20250113_df.columns.difference(['InverterFault']).tolist(), 
        bins='auto', 
        quantiles=[0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]
    )

    # Print bin suggestions in a formatted way
    print("\n📌 Bin Distributions info:")
    for column, stats in bins_result.items():
        print(f"\n🔹 {column}:")
        for key, value in stats.items():
            print(f"   - {key}: {value}")

else:
    print("⚠ Dataset '20250113-imola.csv' not found in the specified folder.")

📂 Loading dataset: .\data\datasets-imola\20250113-imola.csv

🔍 Dataset Overview:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 497553 entries, 0 to 497552
Data columns (total 14 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   BatteryVoltage_V             497553 non-null  float64
 1   BatteryCurrent_A             497553 non-null  float64
 2   BatteryPackTemp_C            497553 non-null  float64
 3   InverterFault                497553 non-null  int64  
 4   InverterSpeed_RearLeft_RPM   497553 non-null  float64
 5   Inverter_Iq_Ref_RearLeft_A   497553 non-null  float64
 6   Inverter_Id_Ref_RearLeft_A   497553 non-null  float64
 7   MotorTemp_RearLeft_C         497553 non-null  float64
 8   InverterTemp_RearLeft_C      497553 non-null  float64
 9   InverterSpeed_RearRight_RPM  497553 non-null  float64
 10  Inverter_Iq_Ref_RearRight_A  497553 non-null  float64
 11  Inverter_Id_Ref_RearRight_A  497553 non-null  float64
 12  MotorTemp_RearRight_C        497553 non-null  float64
 13  InverterTemp_RearRight_C     497553 non-null  float64
dtypes: float64(13), int64(1)
memory usage: 53.1 MB
None

📊 First 5 Rows:

📈 Summary Statistics:

📌 Bin Distributions info:

🔹 BatteryCurrent_A:
   - num_bins: 6
   - quantile_edges: [-13.5, -0.3899999856948852, 0.0, 0.0900000035762786, 4.360000133514404, 20.559999465942383, 115.79000091552734]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 BatteryPackTemp_C:
   - num_bins: 6
   - quantile_edges: [5.190000057220459, 7.090000152587891, 11.489999771118164, 15.729999542236328, 19.270000457763672, 20.68000030517578, 23.63999938964844]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 BatteryVoltage_V:
   - num_bins: 6
   - quantile_edges: [385.8200073242188, 435.2200012207031, 448.3800048828125, 463.5799865722656, 478.8800048828125, 488.8599853515625, 503.1000061035156]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterSpeed_RearLeft_RPM:
   - num_bins: 6
   - quantile_edges: [-1439.0, -2.0, 0.0, 1748.0, 4075.0, 5399.0, 14967.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterSpeed_RearRight_RPM:
   - num_bins: 6
   - quantile_edges: [-573.0, 0.0, 0.0, 1740.0, 4023.0, 5301.0, 10113.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterTemp_RearLeft_C:
   - num_bins: 6
   - quantile_edges: [0.0, 21.0, 24.0, 25.0, 28.0, 33.0, 45.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterTemp_RearRight_C:
   - num_bins: 6
   - quantile_edges: [0.0, 22.0, 24.0, 26.0, 29.0, 34.0, 46.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Id_Ref_RearLeft_A:
   - num_bins: 6
   - quantile_edges: [-52.9375, -4.0, -1.3125, 0.0, 0.0, 0.0, 0.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Id_Ref_RearRight_A:
   - num_bins: 6
   - quantile_edges: [-45.9375, -3.9375, -1.3125, 0.0, 0.0, 0.0, 0.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Iq_Ref_RearLeft_A:
   - num_bins: 6
   - quantile_edges: [-7.0625, -0.125, 0.0, 0.0, 7.75, 22.8125, 52.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Iq_Ref_RearRight_A:
   - num_bins: 6
   - quantile_edges: [-50.5, -22.25, -7.5625, 0.0, 0.0, 0.125, 6.875]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 MotorTemp_RearLeft_C:
   - num_bins: 6
   - quantile_edges: [7.0, 25.0, 29.0, 33.0, 42.0, 46.0, 57.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 MotorTemp_RearRight_C:
   - num_bins: 6
   - quantile_edges: [9.0, 25.0, 30.0, 36.0, 47.0, 53.0, 67.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

# Select the dataset file to analyze
dataset_path = next((file for file in datasets_imola if '20250114-imola.csv' in file), None)

if dataset_path:
    print(f"📂 Loading dataset: {dataset_path}")

    # Load the dataset into a DataFrame
    imola_20250114_df = pd.read_csv(dataset_path)

    # Display basic dataset information
    print("\n🔍 Dataset Overview:")
    print(imola_20250114_df.info())

    print("\n📊 First 5 Rows:")
    display(imola_20250114_df.head())

    print("\n📈 Summary Statistics:")
    display(imola_20250114_df.describe())

    # Get distribution overview with resulting bins
    bins_result = DataVisualizer.plot_distributions_overview(
        df=imola_20250114_df, 
        columns=imola_20250114_df.columns.difference(['InverterFault']).tolist(), 
        bins='auto', 
        quantiles=[0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]
    )

    # Print bin suggestions in a formatted way
    print("\n📌 Bin Distributions info:")
    for column, stats in bins_result.items():
        print(f"\n🔹 {column}:")
        for key, value in stats.items():
            print(f"   - {key}: {value}")

else:
    print("⚠ Dataset '20250114-imola.csv' not found in the specified folder.")

📂 Loading dataset: .\data\datasets-imola\20250114-imola.csv

🔍 Dataset Overview:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 603724 entries, 0 to 603723
Data columns (total 14 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   BatteryVoltage_V             603724 non-null  float64
 1   BatteryCurrent_A             603724 non-null  float64
 2   BatteryPackTemp_C            603724 non-null  float64
 3   InverterFault                603724 non-null  int64  
 4   InverterSpeed_RearLeft_RPM   603724 non-null  float64
 5   Inverter_Iq_Ref_RearLeft_A   603724 non-null  float64
 6   Inverter_Id_Ref_RearLeft_A   603724 non-null  float64
 7   MotorTemp_RearLeft_C         603724 non-null  float64
 8   InverterTemp_RearLeft_C      603724 non-null  float64
 9   InverterSpeed_RearRight_RPM  603724 non-null  float64
 10  Inverter_Iq_Ref_RearRight_A  603724 non-null  float64
 11  Inverter_Id_Ref_RearRight_A  603724 non-null  float64
 12  MotorTemp_RearRight_C        603724 non-null  float64
 13  InverterTemp_RearRight_C     603724 non-null  float64
dtypes: float64(13), int64(1)
memory usage: 64.5 MB
None

📊 First 5 Rows:

📈 Summary Statistics:

📌 Bin Distributions info:

🔹 BatteryCurrent_A:
   - num_bins: 6
   - quantile_edges: [-14.93000030517578, -0.3300000131130218, 0.0299999993294477, 0.0599999986588954, 0.699999988079071, 20.040000915527344, 116.33999633789062]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 BatteryPackTemp_C:
   - num_bins: 6
   - quantile_edges: [15.100000381469728, 16.969999313354492, 18.950000762939453, 28.540000915527344, 30.729999542236328, 31.200000762939453, 33.150001525878906]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 BatteryVoltage_V:
   - num_bins: 6
   - quantile_edges: [405.3999938964844, 454.8999938964844, 460.8800048828125, 478.9400024414063, 488.4599914550781, 494.3599853515625, 503.260009765625]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterSpeed_RearLeft_RPM:
   - num_bins: 6
   - quantile_edges: [-326.0, -2.0, 0.0, 3.0, 3873.0, 5571.0, 15312.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterSpeed_RearRight_RPM:
   - num_bins: 6
   - quantile_edges: [-239.0, 0.0, 0.0, 0.0, 3841.0, 5446.0, 11698.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterTemp_RearLeft_C:
   - num_bins: 6
   - quantile_edges: [0.0, 20.0, 23.0, 26.0, 30.0, 34.0, 45.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 InverterTemp_RearRight_C:
   - num_bins: 6
   - quantile_edges: [0.0, 21.0, 24.0, 27.0, 31.0, 35.0, 46.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Id_Ref_RearLeft_A:
   - num_bins: 6
   - quantile_edges: [-52.9375, -3.875, -0.4375, 0.0, 0.0, 0.0, 0.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Id_Ref_RearRight_A:
   - num_bins: 6
   - quantile_edges: [-46.125, -3.8125, -0.4375, 0.0, 0.0, 0.0, 0.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Iq_Ref_RearLeft_A:
   - num_bins: 6
   - quantile_edges: [-7.625, 0.0, 0.0, 0.0, 1.875, 22.25, 51.875]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 Inverter_Iq_Ref_RearRight_A:
   - num_bins: 6
   - quantile_edges: [-50.375, -21.875, -2.0625, 0.0, 0.0, 0.0, 7.375]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 MotorTemp_RearLeft_C:
   - num_bins: 6
   - quantile_edges: [4.0, 19.0, 27.0, 31.0, 38.0, 45.0, 66.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

🔹 MotorTemp_RearRight_C:
   - num_bins: 6
   - quantile_edges: [4.0, 19.0, 29.0, 34.0, 42.0, 51.0, 77.0]
   - quantiles_used: [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0]

# Configure different discretization for each column
discretizion_columns_configs = {
    'BatteryCurrent_A': {
        'bins': [-np.inf, 0.09, 20., np.inf],
        'labels': ['0_Idle', '1_Normal', '2_High']
    },
    'BatteryPackTemp_C': {
        'bins': [-np.inf, 16., 28., np.inf],
        'labels': ['0_Low', '1_Normal', '2_High']
    },
    'BatteryVoltage_V': {
        'bins': [-np.inf, 450., 480., np.inf],
        'labels': ['0_Low', '1_Medium', '2_High']
    },
    'InverterSpeed_RearLeft_RPM': {
        'bins': [-np.inf, 0., 4000., np.inf],
        'labels': ['0_Idle', '1_Normal_Pace', '2_High_Pace']
    },
    'InverterSpeed_RearRight_RPM': {
        'bins': [-np.inf, 0., 4000., np.inf],
        'labels': ['0_Idle', '1_Normal_Pace', '2_High_Pace']
    },
    'InverterTemp_RearLeft_C': {
        'bins': [-np.inf, 20., 30., np.inf],
        'labels': ['0_Low', '1_Normal', '2_High']
    },
    'InverterTemp_RearRight_C': {
        'bins': [-np.inf, 20., 30., np.inf],
        'labels': ['0_Low', '1_Normal', '2_High']
    },
    'Inverter_Id_Ref_RearLeft_A': {
        'bins': [-np.inf, -4., -1., np.inf],
        'labels': ['0_Very_Negative', '1_Negative', '2_Close_Zero']
    },
    'Inverter_Id_Ref_RearRight_A': {
        'bins': [-np.inf, -4., -1., np.inf],
        'labels': ['0_Very_Negative', '1_Negative', '2_Close_Zero']
    },
    'Inverter_Iq_Ref_RearRight_A': {
        'bins': [-np.inf, -4, 4, np.inf],
        'labels': ['0_Negative', '1_Close_Zero', '2_Positive']
    },
    'Inverter_Iq_Ref_RearLeft_A': {
        'bins': [-np.inf, -4, 4, np.inf],
        'labels': ['0_Negative', '1_Close_Zero', '2_Positive']
    },
    'MotorTemp_RearLeft_C': {
        'bins': [-np.inf, 25., 55., np.inf],
        'labels': ['0_Low', '1_Normal', '2_High']
    },
    'MotorTemp_RearRight_C': {
        'bins': [-np.inf, 25., 55., np.inf],
        'labels': ['0_Low', '1_Normal', '2_High']
    },
}

# Perform discretization on numeric columns using predefined bin configurations
discretization_info, discretized_imola_20241128_df = DataDiscretizer.discretize_columns(
    df=imola_20241128_df, 
    columns_config=discretizion_columns_configs,
    method='cut'
)

# Display summary of discretization results
print("\n📊 Discretization Overview:")
for column, info in discretization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Method: {info['method']}")
    print(f"   - Bin Edges: {info['bin_edges']}")
    print(f"   - Number of Bins: {info['bin_count']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Discretization Overview:

🔹 Column: BatteryCurrent_A
   - Method: Threshold-based
   - Bin Edges: [ -inf  0.09 20.     inf]
   - Number of Bins: 3
   - Value Counts:
BatteryCurrent_A
0_Idle      103222
1_Normal     99478
2_High       45748

🔹 Column: BatteryPackTemp_C
   - Method: Threshold-based
   - Bin Edges: [-inf  16.  28.  inf]
   - Number of Bins: 3
   - Value Counts:
BatteryPackTemp_C
0_Low        44102
1_Normal    143430
2_High       60916

🔹 Column: BatteryVoltage_V
   - Method: Threshold-based
   - Bin Edges: [-inf 450. 480.  inf]
   - Number of Bins: 3
   - Value Counts:
BatteryVoltage_V
0_Low        27047
1_Medium    139398
2_High       82003

🔹 Column: InverterSpeed_RearLeft_RPM
   - Method: Threshold-based
   - Bin Edges: [ -inf    0. 4000.   inf]
   - Number of Bins: 3
   - Value Counts:
InverterSpeed_RearLeft_RPM
0_Idle           89932
1_Normal_Pace    65861
2_High_Pace      92655

🔹 Column: InverterSpeed_RearRight_RPM
   - Method: Threshold-based
   - Bin Edges: [ -inf    0. 4000.   inf]
   - Number of Bins: 3
   - Value Counts:
InverterSpeed_RearRight_RPM
0_Idle           130287
1_Normal_Pace     24379
2_High_Pace       93782

🔹 Column: InverterTemp_RearLeft_C
   - Method: Threshold-based
   - Bin Edges: [-inf  20.  30.  inf]
   - Number of Bins: 3
   - Value Counts:
InverterTemp_RearLeft_C
0_Low        21543
1_Normal    108051
2_High      118854

🔹 Column: InverterTemp_RearRight_C
   - Method: Threshold-based
   - Bin Edges: [-inf  20.  30.  inf]
   - Number of Bins: 3
   - Value Counts:
InverterTemp_RearRight_C
0_Low        21442
1_Normal    104747
2_High      122259

🔹 Column: Inverter_Id_Ref_RearLeft_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.  -1.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Id_Ref_RearLeft_A
0_Very_Negative     33794
1_Negative          48612
2_Close_Zero       166042

🔹 Column: Inverter_Id_Ref_RearRight_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.  -1.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Id_Ref_RearRight_A
0_Very_Negative     29745
1_Negative          55072
2_Close_Zero       163631

🔹 Column: Inverter_Iq_Ref_RearRight_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.   4.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Iq_Ref_RearRight_A
0_Negative       78422
1_Close_Zero    157497
2_Positive       12529

🔹 Column: Inverter_Iq_Ref_RearLeft_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.   4.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Iq_Ref_RearLeft_A
0_Negative        8578
1_Close_Zero    159569
2_Positive       80301

🔹 Column: MotorTemp_RearLeft_C
   - Method: Threshold-based
   - Bin Edges: [-inf  25.  55.  inf]
   - Number of Bins: 3
   - Value Counts:
MotorTemp_RearLeft_C
0_Low        31242
1_Normal    178111
2_High       39095

🔹 Column: MotorTemp_RearRight_C
   - Method: Threshold-based
   - Bin Edges: [-inf  25.  55.  inf]
   - Number of Bins: 3
   - Value Counts:
MotorTemp_RearRight_C
0_Low        29742
1_Normal    162912
2_High       55794

# Perform discretization on numeric columns using predefined bin configurations
binarization_info, discretized_imola_20241128_df = DataDiscretizer.binarize_columns(
    df=discretized_imola_20241128_df, 
    columns=['InverterFault'],
    thresholds={'InverterFault': 0.5},
)

# Display summary of binarization results
print("\n📊 Binarization Overview:")
for column, info in binarization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Threshold: {info['threshold']}")
    print(f"   - True (%): {info['percentage_true']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Binarization Overview:

🔹 Column: InverterFault
   - Threshold: 0.5
   - True (%): 0.0
   - Value Counts:
InverterFault
False    248448

# Display first few rows of discretized data
print("\n📋 Sample of Discretized Data:")
display(discretized_imola_20241128_df.head())

📋 Sample of Discretized Data:

encoded_imola20241128_df, encoding_info = DataEncoder.encode_categorical_columns(
    df=discretized_imola_20241128_df, 
    encoding_strategy='ordinal'
)

DataVisualizer.plot_discrete_distributions(
    df=encoded_imola20241128_df,
    fault_col='InverterFault'
)

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

# Define output file path
output_file = os.path.join(DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, 'discr-20241128-imola.csv')

# Save the discretized dataset
DataProcessor.save_dataset(discretized_imola_20241128_df, output_file, file_format="csv")
print(f"✅ Saved discretized data to: {output_file}")

Dataset with shape (248448, 14), saved successfully at .\data\discretized-datasets-imola\discr-20241128-imola.csv (csv).
✅ Saved discretized data to: .\data\discretized-datasets-imola\discr-20241128-imola.csv

# Perform discretization on numeric columns using predefined bin configurations
discretization_info, discretized_imola_20250113_df = DataDiscretizer.discretize_columns(
    df=imola_20250113_df, 
    columns_config=discretizion_columns_configs,
    method='cut'
)

# Display summary of discretization results
print("\n📊 Discretization Overview:")
for column, info in discretization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Method: {info['method']}")
    print(f"   - Bin Edges: {info['bin_edges']}")
    print(f"   - Number of Bins: {info['bin_count']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Discretization Overview:

🔹 Column: BatteryCurrent_A
   - Method: Threshold-based
   - Bin Edges: [ -inf  0.09 20.     inf]
   - Number of Bins: 3
   - Value Counts:
BatteryCurrent_A
0_Idle      227235
1_Normal    219245
2_High       51073

🔹 Column: BatteryPackTemp_C
   - Method: Threshold-based
   - Bin Edges: [-inf  16.  28.  inf]
   - Number of Bins: 3
   - Value Counts:
BatteryPackTemp_C
0_Low       268152
1_Normal    229401
2_High           0

🔹 Column: BatteryVoltage_V
   - Method: Threshold-based
   - Bin Edges: [-inf 450. 480.  inf]
   - Number of Bins: 3
   - Value Counts:
BatteryVoltage_V
0_Low       141034
1_Medium    244765
2_High      111754

🔹 Column: InverterSpeed_RearLeft_RPM
   - Method: Threshold-based
   - Bin Edges: [ -inf    0. 4000.   inf]
   - Number of Bins: 3
   - Value Counts:
InverterSpeed_RearLeft_RPM
0_Idle           147953
1_Normal_Pace    220363
2_High_Pace      129237

🔹 Column: InverterSpeed_RearRight_RPM
   - Method: Threshold-based
   - Bin Edges: [ -inf    0. 4000.   inf]
   - Number of Bins: 3
   - Value Counts:
InverterSpeed_RearRight_RPM
0_Idle           213562
1_Normal_Pace    158162
2_High_Pace      125829

🔹 Column: InverterTemp_RearLeft_C
   - Method: Threshold-based
   - Bin Edges: [-inf  20.  30.  inf]
   - Number of Bins: 3
   - Value Counts:
InverterTemp_RearLeft_C
0_Low        41664
1_Normal    369706
2_High       86183

🔹 Column: InverterTemp_RearRight_C
   - Method: Threshold-based
   - Bin Edges: [-inf  20.  30.  inf]
   - Number of Bins: 3
   - Value Counts:
InverterTemp_RearRight_C
0_Low        32166
1_Normal    369644
2_High       95743

🔹 Column: Inverter_Id_Ref_RearLeft_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.  -1.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Id_Ref_RearLeft_A
0_Very_Negative     50226
1_Negative          90760
2_Close_Zero       356567

🔹 Column: Inverter_Id_Ref_RearRight_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.  -1.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Id_Ref_RearRight_A
0_Very_Negative     48531
1_Negative          92083
2_Close_Zero       356939

🔹 Column: Inverter_Iq_Ref_RearRight_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.   4.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Iq_Ref_RearRight_A
0_Negative      155024
1_Close_Zero    341904
2_Positive         625

🔹 Column: Inverter_Iq_Ref_RearLeft_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.   4.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Iq_Ref_RearLeft_A
0_Negative         751
1_Close_Zero    341385
2_Positive      155417

🔹 Column: MotorTemp_RearLeft_C
   - Method: Threshold-based
   - Bin Edges: [-inf  25.  55.  inf]
   - Number of Bins: 3
   - Value Counts:
MotorTemp_RearLeft_C
0_Low        55209
1_Normal    440648
2_High        1696

🔹 Column: MotorTemp_RearRight_C
   - Method: Threshold-based
   - Bin Edges: [-inf  25.  55.  inf]
   - Number of Bins: 3
   - Value Counts:
MotorTemp_RearRight_C
0_Low        50555
1_Normal    412212
2_High       34786

# Perform discretization on numeric columns using predefined bin configurations
binarization_info, discretized_imola_20250113_df = DataDiscretizer.binarize_columns(
    df=discretized_imola_20250113_df, 
    columns=['InverterFault'],
    thresholds={'InverterFault': 0.5},
)

# Display summary of binarization results
print("\n📊 Binarization Overview:")
for column, info in binarization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Threshold: {info['threshold']}")
    print(f"   - True (%): {info['percentage_true']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Binarization Overview:

🔹 Column: InverterFault
   - Threshold: 0.5
   - True (%): 11.620872550260977
   - Value Counts:
InverterFault
False    439733
True      57820

# Display first few rows of discretized data
print("\n📋 Sample of Discretized Data:")
display(discretized_imola_20250113_df.head())

📋 Sample of Discretized Data:

# Encode categorical columns using the specified encoding strategy
encoded_imola_20250113_df, _ = DataEncoder.encode_categorical_columns(
    df=discretized_imola_20250113_df, 
    encoding_strategy='ordinal'
)

# Visualize the distribution of discrete variables with respect to 'InverterFault'
DataVisualizer.plot_discrete_distributions(
    df=encoded_imola_20250113_df,
    fault_col='InverterFault'
)

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

# Define output file path
output_file = os.path.join(DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, 'discr-20250113-imola.csv')

# Save the discretized dataset
DataProcessor.save_dataset(discretized_imola_20250113_df, output_file, file_format="csv")
print(f"✅ Saved discretized data to: {output_file}")

Dataset with shape (497553, 14), saved successfully at .\data\discretized-datasets-imola\discr-20250113-imola.csv (csv).
✅ Saved discretized data to: .\data\discretized-datasets-imola\discr-20250113-imola.csv

# Perform discretization on numeric columns using predefined bin configurations
discretization_info, discretized_imola_20250114_df = DataDiscretizer.discretize_columns(
    df=imola_20250114_df, 
    columns_config=discretizion_columns_configs,
    method='cut'
)

# Display summary of discretization results
print("\n📊 Discretization Overview:")
for column, info in discretization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Method: {info['method']}")
    print(f"   - Bin Edges: {info['bin_edges']}")
    print(f"   - Number of Bins: {info['bin_count']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Discretization Overview:

🔹 Column: BatteryCurrent_A
   - Method: Threshold-based
   - Bin Edges: [ -inf  0.09 20.     inf]
   - Number of Bins: 3
   - Value Counts:
BatteryCurrent_A
0_Idle      365956
1_Normal    177326
2_High       60442

🔹 Column: BatteryPackTemp_C
   - Method: Threshold-based
   - Bin Edges: [-inf  16.  28.  inf]
   - Number of Bins: 3
   - Value Counts:
BatteryPackTemp_C
0_Low        21071
1_Normal    240217
2_High      342436

🔹 Column: BatteryVoltage_V
   - Method: Threshold-based
   - Bin Edges: [-inf 450. 480.  inf]
   - Number of Bins: 3
   - Value Counts:
BatteryVoltage_V
0_Low        48085
1_Medium    266979
2_High      288660

🔹 Column: InverterSpeed_RearLeft_RPM
   - Method: Threshold-based
   - Bin Edges: [ -inf    0. 4000.   inf]
   - Number of Bins: 3
   - Value Counts:
InverterSpeed_RearLeft_RPM
0_Idle           220654
1_Normal_Pace    239224
2_High_Pace      143846

🔹 Column: InverterSpeed_RearRight_RPM
   - Method: Threshold-based
   - Bin Edges: [ -inf    0. 4000.   inf]
   - Number of Bins: 3
   - Value Counts:
InverterSpeed_RearRight_RPM
0_Idle           322972
1_Normal_Pace    139467
2_High_Pace      141285

🔹 Column: InverterTemp_RearLeft_C
   - Method: Threshold-based
   - Bin Edges: [-inf  20.  30.  inf]
   - Number of Bins: 3
   - Value Counts:
InverterTemp_RearLeft_C
0_Low        67147
1_Normal    402530
2_High      134047

🔹 Column: InverterTemp_RearRight_C
   - Method: Threshold-based
   - Bin Edges: [-inf  20.  30.  inf]
   - Number of Bins: 3
   - Value Counts:
InverterTemp_RearRight_C
0_Low        56755
1_Normal    391176
2_High      155793

🔹 Column: Inverter_Id_Ref_RearLeft_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.  -1.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Id_Ref_RearLeft_A
0_Very_Negative     58818
1_Negative          72005
2_Close_Zero       472901

🔹 Column: Inverter_Id_Ref_RearRight_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.  -1.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Id_Ref_RearRight_A
0_Very_Negative     57328
1_Negative          73730
2_Close_Zero       472666

🔹 Column: Inverter_Iq_Ref_RearRight_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.   4.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Iq_Ref_RearRight_A
0_Negative      140283
1_Close_Zero    462711
2_Positive         730

🔹 Column: Inverter_Iq_Ref_RearLeft_A
   - Method: Threshold-based
   - Bin Edges: [-inf  -4.   4.  inf]
   - Number of Bins: 3
   - Value Counts:
Inverter_Iq_Ref_RearLeft_A
0_Negative         823
1_Close_Zero    462841
2_Positive      140060

🔹 Column: MotorTemp_RearLeft_C
   - Method: Threshold-based
   - Bin Edges: [-inf  25.  55.  inf]
   - Number of Bins: 3
   - Value Counts:
MotorTemp_RearLeft_C
0_Low       112472
1_Normal    469609
2_High       21643

🔹 Column: MotorTemp_RearRight_C
   - Method: Threshold-based
   - Bin Edges: [-inf  25.  55.  inf]
   - Number of Bins: 3
   - Value Counts:
MotorTemp_RearRight_C
0_Low        84316
1_Normal    483568
2_High       35840

# Perform discretization on numeric columns using predefined bin configurations
binarization_info, discretized_imola_20250114_df = DataDiscretizer.binarize_columns(
    df=discretized_imola_20250114_df, 
    columns=['InverterFault'],
    thresholds={'InverterFault': 0.5},
)

# Display summary of binarization results
print("\n📊 Binarization Overview:")
for column, info in binarization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Threshold: {info['threshold']}")
    print(f"   - True (%): {info['percentage_true']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Binarization Overview:

🔹 Column: InverterFault
   - Threshold: 0.5
   - True (%): 12.56931975538491
   - Value Counts:
InverterFault
False    527840
True      75884

# Display first few rows of discretized data
print("\n📋 Sample of Discretized Data:")
display(discretized_imola_20250114_df.head())

📋 Sample of Discretized Data:

# Encode categorical columns using the specified encoding strategy
encoded_imola_20250114_df, _ = DataEncoder.encode_categorical_columns(
    df=discretized_imola_20250114_df, 
    encoding_strategy='ordinal'
)

# Visualize the distribution of discrete variables with respect to 'InverterFault'
DataVisualizer.plot_discrete_distributions(
    df=encoded_imola_20250114_df,
    fault_col='InverterFault'
)

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

# Define output file path
output_file = os.path.join(DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, 'discr-20250114-imola.csv')

# Save the discretized dataset
DataProcessor.save_dataset(discretized_imola_20250114_df, output_file, file_format="csv")
print(f"✅ Saved discretized data to: {output_file}")

Dataset with shape (603724, 14), saved successfully at .\data\discretized-datasets-imola\discr-20250114-imola.csv (csv).
✅ Saved discretized data to: .\data\discretized-datasets-imola\discr-20250114-imola.csv

# Define the number of bins for each feature (K-Means discretization)
kmeans_bins = {
    'BatteryVoltage_V': 3,
    'BatteryCurrent_A': 3,
    'BatteryPackTemp_C': 4,
    'InverterSpeed_RearLeft_RPM': 6,
    'Inverter_Iq_Ref_RearLeft_A': 3,
    'Inverter_Id_Ref_RearLeft_A': 6,
    'MotorTemp_RearLeft_C': 4,
    'InverterTemp_RearLeft_C': 7,
    'InverterSpeed_RearRight_RPM': 6,
    'Inverter_Iq_Ref_RearRight_A': 3,
    'Inverter_Id_Ref_RearRight_A': 6,
    'MotorTemp_RearRight_C': 4,
    'InverterTemp_RearRight_C': 7,
}

# Perform K-Means discretization
kmeans_discretized_imola_20241128_df, kmeans_result = DataDiscretizer.discretize_kmeans(
    df=imola_20241128_df.drop(columns=['InverterFault']), 
    n_bins=list(kmeans_bins.values())  # Extract bin values in correct order
)

# Display K-Means discretization results in a readable format
print("\n" + "="*80)
print("🔍 K-Means Discretization Summary")
print("="*80)

for col, details in kmeans_result.items():
    print(f"\n📌 Feature: {col}")
    print("-" * 60)
    print(f"  📊 Method: {details['method']}")
    print(f"  🔢 Number of Bins: {details['bin_count']}")
    print(f"  📍 Bin Edges: {details['bin_edges']}")
    print(f"  🎯 Cluster Centers: {details['cluster_centers']}")
    print(f"  📈 Value Counts: {details['value_counts']}")
    print(f"  🔄 Mapping: {details['mapping']}")
    print("-" * 60)

Processing column: BatteryVoltage_V
BatteryVoltage_V: Using provided n_bins: 3
BatteryVoltage_V discretized into 3 bins.
Processing column: BatteryCurrent_A
BatteryCurrent_A: Using provided n_bins: 3
BatteryCurrent_A discretized into 3 bins.
Processing column: BatteryPackTemp_C
BatteryPackTemp_C: Using provided n_bins: 4
BatteryPackTemp_C discretized into 4 bins.
Processing column: InverterSpeed_RearLeft_RPM
InverterSpeed_RearLeft_RPM: Using provided n_bins: 6
InverterSpeed_RearLeft_RPM discretized into 6 bins.
Processing column: Inverter_Iq_Ref_RearLeft_A
Inverter_Iq_Ref_RearLeft_A: Using provided n_bins: 3
Inverter_Iq_Ref_RearLeft_A discretized into 3 bins.
Processing column: Inverter_Id_Ref_RearLeft_A
Inverter_Id_Ref_RearLeft_A: Using provided n_bins: 6
Inverter_Id_Ref_RearLeft_A discretized into 6 bins.
Processing column: MotorTemp_RearLeft_C
MotorTemp_RearLeft_C: Using provided n_bins: 4
MotorTemp_RearLeft_C discretized into 4 bins.
Processing column: InverterTemp_RearLeft_C
InverterTemp_RearLeft_C: Using provided n_bins: 7
InverterTemp_RearLeft_C discretized into 7 bins.
Processing column: InverterSpeed_RearRight_RPM
InverterSpeed_RearRight_RPM: Using provided n_bins: 6
InverterSpeed_RearRight_RPM discretized into 6 bins.
Processing column: Inverter_Iq_Ref_RearRight_A
Inverter_Iq_Ref_RearRight_A: Using provided n_bins: 3
Inverter_Iq_Ref_RearRight_A discretized into 3 bins.
Processing column: Inverter_Id_Ref_RearRight_A
Inverter_Id_Ref_RearRight_A: Using provided n_bins: 6
Inverter_Id_Ref_RearRight_A discretized into 6 bins.
Processing column: MotorTemp_RearRight_C
MotorTemp_RearRight_C: Using provided n_bins: 4
MotorTemp_RearRight_C discretized into 4 bins.
Processing column: InverterTemp_RearRight_C
InverterTemp_RearRight_C: Using provided n_bins: 7
InverterTemp_RearRight_C discretized into 7 bins.

================================================================================
🔍 K-Means Discretization Summary
================================================================================

📌 Feature: BatteryVoltage_V
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [413.5, 457.6799926757813, 479.6400146484375, 510.7799987792969]
  🎯 Cluster Centers: [450.8841660643744, 493.16844684195354, 470.7280725710292]
  📈 Value Counts: {0: 94204, 1: 77240, 2: 77004}
  🔄 Mapping: {0: (413.5, 457.6799926757813), 1: (457.6799926757813, 479.6400146484375), 2: (479.6400146484375, 510.7799987792969)}
------------------------------------------------------------

📌 Feature: BatteryCurrent_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-36.68999862670898, 0.0599999986588954, 0.6200000047686626, 115.23999786376952]
  🎯 Cluster Centers: [0.5426547254437413, 30.27464127164916, 77.05537592619686]
  📈 Value Counts: {0: 194991, 1: 42069, 2: 11388}
  🔄 Mapping: {0: (-36.68999862670898, 0.0599999986588954), 1: (0.0599999986588954, 0.6200000047686626), 2: (0.6200000047686626, 115.23999786376952)}
------------------------------------------------------------

📌 Feature: BatteryPackTemp_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [13.75, 16.75, 21.959999084472656, 27.81999969482422, 29.68000030517578]
  🎯 Cluster Centers: [28.616491927578554, 15.3777839255211, 24.45629642325104, 20.78825607443877]
  📈 Value Counts: {0: 82233, 1: 81874, 2: 36646, 3: 47695}
  🔄 Mapping: {0: (13.75, 16.75), 1: (16.75, 21.959999084472656), 2: (21.959999084472656, 27.81999969482422), 3: (27.81999969482422, 29.68000030517578)}
------------------------------------------------------------

📌 Feature: InverterSpeed_RearLeft_RPM
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-76.0, -1.0, 0.0, 3.0, 4769.0, 6867.0, 14881.0]
  🎯 Cluster Centers: [20.62962072867913, 6629.352946016759, 2974.976158847773, 10430.721695374417, 4995.9910128912, 8034.712264545499]
  📈 Value Counts: {0: 141459, 1: 36646, 2: 14210, 3: 5106, 4: 27153, 5: 23874}
  🔄 Mapping: {0: (-76.0, -1.0), 1: (-1.0, 0.0), 2: (0.0, 3.0), 3: (3.0, 4769.0), 4: (4769.0, 6867.0), 5: (6867.0, 14881.0)}
------------------------------------------------------------

📌 Feature: Inverter_Iq_Ref_RearLeft_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-26.75, 0.0, 2.8125, 52.0]
  🎯 Cluster Centers: [-0.03620901408857424, 37.62631716450713, 17.729650426414203]
  📈 Value Counts: {0: 178110, 1: 22966, 2: 47372}
  🔄 Mapping: {0: (-26.75, 0.0), 1: (0.0, 2.8125), 2: (2.8125, 52.0)}
------------------------------------------------------------

📌 Feature: Inverter_Id_Ref_RearLeft_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-49.8125, -3.4375, -0.9375, 0.0, 0.0, 0.0, 0.0]
  🎯 Cluster Centers: [-0.10410681716289272, -8.536405968033035, -4.8914872472839095, -35.04382313231325, -20.93918398768281, -2.425968266831328]
  📈 Value Counts: {0: 171611, 1: 7758, 2: 27798, 3: 1111, 4: 1299, 5: 38871}
  🔄 Mapping: {0: (-49.8125, -3.4375), 1: (-3.4375, -0.9375), 2: (-0.9375, 0.0), 3: (0.0, 0.0), 4: (0.0, 0.0), 5: (0.0, 0.0)}
------------------------------------------------------------

📌 Feature: MotorTemp_RearLeft_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [12.0, 32.0, 39.0, 49.0, 76.0]
  🎯 Cluster Centers: [33.73333615158966, 63.31082826929664, 13.909595926208517, 45.822453446143655]
  📈 Value Counts: {0: 94621, 1: 41798, 2: 30242, 3: 81787}
  🔄 Mapping: {0: (12.0, 32.0), 1: (32.0, 39.0), 2: (39.0, 49.0), 3: (49.0, 76.0)}
------------------------------------------------------------

📌 Feature: InverterTemp_RearLeft_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 7
  📍 Bin Edges: [15.0, 24.0, 28.0, 29.0, 31.0, 33.0, 34.0, 45.0]
  🎯 Cluster Centers: [28.29360564341743, 33.80628629996717, 16.793900570946256, 24.46665411207726, 41.71512081060014, 30.985814302383712, 37.24841229286772]
  📈 Value Counts: {0: 52734, 1: 55613, 2: 21543, 3: 37171, 4: 7698, 5: 59990, 6: 13699}
  🔄 Mapping: {0: (15.0, 24.0), 1: (24.0, 28.0), 2: (28.0, 29.0), 3: (29.0, 31.0), 4: (31.0, 33.0), 5: (33.0, 34.0), 6: (34.0, 45.0)}
------------------------------------------------------------

📌 Feature: InverterSpeed_RearRight_RPM
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-11.0, 0.0, 0.0, 0.0, 4794.0, 6693.0, 12655.0]
  🎯 Cluster Centers: [18.35260185393554, 7562.477915137873, 6057.463134942713, 2586.238049940551, 9584.833135938898, 4453.473028507175]
  📈 Value Counts: {0: 141088, 1: 31002, 2: 37179, 3: 8321, 4: 7599, 5: 23259}
  🔄 Mapping: {0: (-11.0, 0.0), 1: (0.0, 0.0), 2: (0.0, 0.0), 3: (0.0, 4794.0), 4: (4794.0, 6693.0), 5: (6693.0, 12655.0)}
------------------------------------------------------------

📌 Feature: Inverter_Iq_Ref_RearRight_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-50.5, -1.5625, 0.0, 32.875]
  🎯 Cluster Centers: [0.3667427478488543, -16.387898260201688, -34.52940134037582]
  📈 Value Counts: {0: 178154, 1: 47017, 2: 23277}
  🔄 Mapping: {0: (-50.5, -1.5625), 1: (-1.5625, 0.0), 2: (0.0, 32.875)}
------------------------------------------------------------

📌 Feature: Inverter_Id_Ref_RearRight_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-45.375, -3.1875, -1.0625, 0.0, 0.0, 0.0, 0.0]
  🎯 Cluster Centers: [-2.194971661268907, -0.08769584661903052, -31.31338304552601, -4.467119527206286, -18.326174242424344, -7.674653057119889]
  📈 Value Counts: {0: 41992, 1: 167478, 2: 1274, 3: 28596, 4: 1650, 5: 7458}
  🔄 Mapping: {0: (-45.375, -3.1875), 1: (-3.1875, -1.0625), 2: (-1.0625, 0.0), 3: (0.0, 0.0), 4: (0.0, 0.0), 5: (0.0, 0.0)}
------------------------------------------------------------

📌 Feature: MotorTemp_RearRight_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [13.0, 34.0, 42.0, 54.0, 80.0]
  🎯 Cluster Centers: [50.81639795517266, 36.31441086634455, 68.33492622608333, 14.891567480341838]
  📈 Value Counts: {0: 76290, 1: 101819, 2: 40597, 3: 29742}
  🔄 Mapping: {0: (13.0, 34.0), 1: (34.0, 42.0), 2: (42.0, 54.0), 3: (54.0, 80.0)}
------------------------------------------------------------

📌 Feature: InverterTemp_RearRight_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 7
  📍 Bin Edges: [15.0, 24.0, 28.0, 29.0, 31.0, 33.0, 34.0, 44.0]
  🎯 Cluster Centers: [40.15338860850781, 30.95595754944509, 24.60902791639487, 15.345998848588128, 34.208817829458326, 28.27070743333499, 19.113807704225827]
  📈 Value Counts: {0: 11096, 1: 62190, 2: 35069, 3: 12159, 4: 68112, 5: 50139, 6: 9683}
  🔄 Mapping: {0: (15.0, 24.0), 1: (24.0, 28.0), 2: (28.0, 29.0), 3: (29.0, 31.0), 4: (31.0, 33.0), 5: (33.0, 34.0), 6: (34.0, 44.0)}
------------------------------------------------------------

# Perform discretization on numeric columns using predefined bin configurations
binarization_info, binarized_fault_df = DataDiscretizer.binarize_columns(
    df=imola_20241128_df['InverterFault'].to_frame(),
    columns=['InverterFault'],
    thresholds={'InverterFault': 0.5},
    labels={'InverterFault': [0, 1]}
)

# Add the binzarized column to discretized dataframe
kmeans_discretized_imola_20241128_df['InverterFault'] = binarized_fault_df['InverterFault']

# Display summary of binarization results
print("\n📊 Binarization Overview:")
for column, info in binarization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Threshold: {info['threshold']}")
    print(f"   - True (%): {info['percentage_true']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Binarization Overview:

🔹 Column: InverterFault
   - Threshold: 0.5
   - True (%): 0.0
   - Value Counts:
InverterFault
0    248448

# Encode categorical columns using the specified encoding strategy
encoded_imola_20241128_df, _ = DataEncoder.encode_categorical_columns(
    df=kmeans_discretized_imola_20241128_df, 
    encoding_strategy='ordinal'
)

# Visualize the distribution of discrete variables with respect to 'InverterFault'
DataVisualizer.plot_discrete_distributions(
    df=encoded_imola_20241128_df,
    fault_col='InverterFault'
)

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

# Define output file path
output_file = os.path.join(DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, 'kmeans', 'discr-20241128-imola.csv')

# Save the discretized dataset
DataProcessor.save_dataset(kmeans_discretized_imola_20241128_df, output_file, file_format="csv")
print(f"✅ Saved discretized (KMeans) data to: {output_file}")

Dataset with shape (248448, 14), saved successfully at .\data\discretized-datasets-imola\kmeans\discr-20241128-imola.csv (csv).
✅ Saved discretized (KMeans) data to: .\data\discretized-datasets-imola\kmeans\discr-20241128-imola.csv

# Perform K-Means discretization
kmeans_discretized_imola_20250113_df, kmeans_result = DataDiscretizer.discretize_kmeans(
    df=imola_20250113_df.drop(columns=['InverterFault']), 
    n_bins=list(kmeans_bins.values())  # Extract bin values in correct order
)

# Display K-Means discretization results in a readable format
print("\n" + "="*80)
print("🔍 K-Means Discretization Summary")
print("="*80)

for col, details in kmeans_result.items():
    print(f"\n📌 Feature: {col}")
    print("-" * 60)
    print(f"  📊 Method: {details['method']}")
    print(f"  🔢 Number of Bins: {details['bin_count']}")
    print(f"  📍 Bin Edges: {details['bin_edges']}")
    print(f"  🎯 Cluster Centers: {details['cluster_centers']}")
    print(f"  📈 Value Counts: {details['value_counts']}")
    print(f"  🔄 Mapping: {details['mapping']}")
    print("-" * 60)

Processing column: BatteryVoltage_V
BatteryVoltage_V: Using provided n_bins: 3
BatteryVoltage_V discretized into 3 bins.
Processing column: BatteryCurrent_A
BatteryCurrent_A: Using provided n_bins: 3
BatteryCurrent_A discretized into 3 bins.
Processing column: BatteryPackTemp_C
BatteryPackTemp_C: Using provided n_bins: 4
BatteryPackTemp_C discretized into 4 bins.
Processing column: InverterSpeed_RearLeft_RPM
InverterSpeed_RearLeft_RPM: Using provided n_bins: 6
InverterSpeed_RearLeft_RPM discretized into 6 bins.
Processing column: Inverter_Iq_Ref_RearLeft_A
Inverter_Iq_Ref_RearLeft_A: Using provided n_bins: 3
Inverter_Iq_Ref_RearLeft_A discretized into 3 bins.
Processing column: Inverter_Id_Ref_RearLeft_A
Inverter_Id_Ref_RearLeft_A: Using provided n_bins: 6
Inverter_Id_Ref_RearLeft_A discretized into 6 bins.
Processing column: MotorTemp_RearLeft_C
MotorTemp_RearLeft_C: Using provided n_bins: 4
MotorTemp_RearLeft_C discretized into 4 bins.
Processing column: InverterTemp_RearLeft_C
InverterTemp_RearLeft_C: Using provided n_bins: 7
InverterTemp_RearLeft_C discretized into 7 bins.
Processing column: InverterSpeed_RearRight_RPM
InverterSpeed_RearRight_RPM: Using provided n_bins: 6
InverterSpeed_RearRight_RPM discretized into 6 bins.
Processing column: Inverter_Iq_Ref_RearRight_A
Inverter_Iq_Ref_RearRight_A: Using provided n_bins: 3
Inverter_Iq_Ref_RearRight_A discretized into 3 bins.
Processing column: Inverter_Id_Ref_RearRight_A
Inverter_Id_Ref_RearRight_A: Using provided n_bins: 6
Inverter_Id_Ref_RearRight_A discretized into 6 bins.
Processing column: MotorTemp_RearRight_C
MotorTemp_RearRight_C: Using provided n_bins: 4
MotorTemp_RearRight_C discretized into 4 bins.
Processing column: InverterTemp_RearRight_C
InverterTemp_RearRight_C: Using provided n_bins: 7
InverterTemp_RearRight_C discretized into 7 bins.

================================================================================
🔍 K-Means Discretization Summary
================================================================================

📌 Feature: BatteryVoltage_V
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [385.8200073242188, 453.8200073242188, 473.8800048828125, 503.1000061035156]
  🎯 Cluster Centers: [463.57189172733916, 486.486566417157, 438.74000215180104]
  📈 Value Counts: {0: 191299, 1: 158915, 2: 147339}
  🔄 Mapping: {0: (385.8200073242188, 453.8200073242188), 1: (453.8200073242188, 473.8800048828125), 2: (473.8800048828125, 503.1000061035156)}
------------------------------------------------------------

📌 Feature: BatteryCurrent_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-13.5, 0.0299999993294477, 1.090000033378601, 115.79000091552734]
  🎯 Cluster Centers: [1.1704989269415709, 25.64220777929068, 68.675531492679]
  📈 Value Counts: {0: 426140, 1: 55553, 2: 15860}
  🔄 Mapping: {0: (-13.5, 0.0299999993294477), 1: (0.0299999993294477, 1.090000033378601), 2: (1.090000033378601, 115.79000091552734)}
------------------------------------------------------------

📌 Feature: BatteryPackTemp_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [5.190000057220459, 11.489999771118164, 15.729999542236328, 19.270000457763672, 23.63999938964844]
  🎯 Cluster Centers: [16.574878316087663, 7.251553135798417, 12.646073472343842, 20.727677360132574]
  📈 Value Counts: {0: 155692, 1: 90482, 2: 118091, 3: 133288}
  🔄 Mapping: {0: (5.190000057220459, 11.489999771118164), 1: (11.489999771118164, 15.729999542236328), 2: (15.729999542236328, 19.270000457763672), 3: (19.270000457763672, 23.63999938964844)}
------------------------------------------------------------

📌 Feature: InverterSpeed_RearLeft_RPM
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-1439.0, -1.0, 1.0, 1748.0, 3440.0, 4773.0, 14967.0]
  🎯 Cluster Centers: [6145.221053681564, 23.614864468717315, 3502.5129763055565, 2254.090649862672, 8847.434798270768, 4735.365005369279]
  📈 Value Counts: {0: 39808, 1: 239874, 2: 78946, 3: 56930, 4: 8245, 5: 73750}
  🔄 Mapping: {0: (-1439.0, -1.0), 1: (-1.0, 1.0), 2: (1.0, 1748.0), 3: (1748.0, 3440.0), 4: (3440.0, 4773.0), 5: (4773.0, 14967.0)}
------------------------------------------------------------

📌 Feature: Inverter_Iq_Ref_RearLeft_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-7.0625, 0.0, 2.4375, 52.0]
  🎯 Cluster Centers: [0.660740561115186, 16.28424835630546, 37.76631710856252]
  📈 Value Counts: {0: 378885, 1: 80763, 2: 37905}
  🔄 Mapping: {0: (-7.0625, 0.0), 1: (0.0, 2.4375), 2: (2.4375, 52.0)}
------------------------------------------------------------

📌 Feature: Inverter_Id_Ref_RearLeft_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-52.9375, -2.5625, -0.5, 0.0, 0.0, 0.0, 0.0]
  🎯 Cluster Centers: [-0.08436387709837745, -4.577186134785373, -19.766811389337715, -2.091052557345818, -7.734198063287585, -33.64936440677975]
  📈 Value Counts: {0: 362147, 1: 41889, 2: 1238, 3: 72673, 4: 18898, 5: 708}
  🔄 Mapping: {0: (-52.9375, -2.5625), 1: (-2.5625, -0.5), 2: (-0.5, 0.0), 3: (0.0, 0.0), 4: (0.0, 0.0), 5: (0.0, 0.0)}
------------------------------------------------------------

📌 Feature: MotorTemp_RearLeft_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [7.0, 29.0, 33.0, 42.0, 57.0]
  🎯 Cluster Centers: [37.20963659214347, 10.808418868734584, 46.09639515312672, 29.257080727488095]
  📈 Value Counts: {0: 112986, 1: 36632, 2: 125359, 3: 222576}
  🔄 Mapping: {0: (7.0, 29.0), 1: (29.0, 33.0), 2: (33.0, 42.0), 3: (42.0, 57.0)}
------------------------------------------------------------

📌 Feature: InverterTemp_RearLeft_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 7
  📍 Bin Edges: [0.0, 22.0, 24.0, 25.0, 25.0, 28.0, 32.0, 45.0]
  🎯 Cluster Centers: [24.754142788583945, 37.25794094174108, 11.042696494079383, 31.75548726953521, 22.37958086758254, 27.918712756767647, 19.087277811067025]
  📈 Value Counts: {0: 212538, 1: 37590, 2: 16629, 3: 63784, 4: 73676, 5: 68301, 6: 25035}
  🔄 Mapping: {0: (0.0, 22.0), 1: (22.0, 24.0), 2: (24.0, 25.0), 3: (25.0, 25.0), 4: (25.0, 28.0), 5: (28.0, 32.0), 6: (32.0, 45.0)}
------------------------------------------------------------

📌 Feature: InverterSpeed_RearRight_RPM
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-573.0, 0.0, 0.0, 1740.0, 3412.0, 4670.0, 10113.0]
  🎯 Cluster Centers: [3404.440648373218, 24.222514019922983, 5969.147318248084, 2178.6841410450065, 4613.262369869755, 8172.621650705116]
  📈 Value Counts: {0: 80440, 1: 239192, 2: 40866, 3: 52637, 4: 75064, 5: 9354}
  🔄 Mapping: {0: (-573.0, 0.0), 1: (0.0, 0.0), 2: (0.0, 1740.0), 3: (1740.0, 3412.0), 4: (3412.0, 4670.0), 5: (4670.0, 10113.0)}
------------------------------------------------------------

📌 Feature: Inverter_Iq_Ref_RearRight_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-50.5, -2.5, 0.0, 6.875]
  🎯 Cluster Centers: [-0.6219269492611454, -36.66034967066401, -15.67274023674117]
  📈 Value Counts: {0: 377231, 1: 38715, 2: 81607}
  🔄 Mapping: {0: (-50.5, -2.5), 1: (-2.5, 0.0), 2: (0.0, 6.875)}
------------------------------------------------------------

📌 Feature: Inverter_Id_Ref_RearRight_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-45.9375, -2.5, -0.4375, 0.0, 0.0, 0.0, 0.0]
  🎯 Cluster Centers: [-0.07881202377394536, -7.499628380124779, -20.086969111969122, -2.0071903333557284, -34.17778649921508, -4.423834872734697]
  📈 Value Counts: {0: 360386, 1: 19341, 2: 1295, 3: 73345, 4: 637, 5: 42549}
  🔄 Mapping: {0: (-45.9375, -2.5), 1: (-2.5, -0.4375), 2: (-0.4375, 0.0), 3: (0.0, 0.0), 4: (0.0, 0.0), 5: (0.0, 0.0)}
------------------------------------------------------------

📌 Feature: MotorTemp_RearRight_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [9.0, 30.0, 36.0, 47.0, 67.0]
  🎯 Cluster Centers: [40.664866271368155, 12.336165048540362, 52.94671602711386, 30.455701838373397]
  📈 Value Counts: {0: 126824, 1: 35432, 2: 126417, 3: 208880}
  🔄 Mapping: {0: (9.0, 30.0), 1: (30.0, 36.0), 2: (36.0, 47.0), 3: (47.0, 67.0)}
------------------------------------------------------------

📌 Feature: InverterTemp_RearRight_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 7
  📍 Bin Edges: [0.0, 23.0, 24.0, 25.0, 27.0, 28.0, 32.0, 46.0]
  🎯 Cluster Centers: [33.33836957363041, 19.69597027058333, 25.8265265627967, 11.857350432747063, 23.40151820011179, 37.765118809449774, 29.168481139930567]
  📈 Value Counts: {0: 51692, 1: 25833, 2: 173871, 3: 15829, 4: 109340, 5: 28491, 6: 92497}
  🔄 Mapping: {0: (0.0, 23.0), 1: (23.0, 24.0), 2: (24.0, 25.0), 3: (25.0, 27.0), 4: (27.0, 28.0), 5: (28.0, 32.0), 6: (32.0, 46.0)}
------------------------------------------------------------

# Perform discretization on numeric columns using predefined bin configurations
binarization_info, binarized_fault_df = DataDiscretizer.binarize_columns(
    df=imola_20250113_df['InverterFault'].to_frame(),
    columns=['InverterFault'],
    thresholds={'InverterFault': 0.5},
    labels={'InverterFault': [0, 1]}
)

# Add the binzarized column to discretized dataframe
kmeans_discretized_imola_20250113_df['InverterFault'] = binarized_fault_df['InverterFault']

# Display summary of binarization results
print("\n📊 Binarization Overview:")
for column, info in binarization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Threshold: {info['threshold']}")
    print(f"   - True (%): {info['percentage_true']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Binarization Overview:

🔹 Column: InverterFault
   - Threshold: 0.5
   - True (%): 11.620872550260977
   - Value Counts:
InverterFault
0    439733
1     57820

# Encode categorical columns using the specified encoding strategy
encoded_imola_20250113_df, _ = DataEncoder.encode_categorical_columns(
    df=kmeans_discretized_imola_20250113_df, 
    encoding_strategy='ordinal'
)

# Visualize the distribution of discrete variables with respect to 'InverterFault'
DataVisualizer.plot_discrete_distributions(
    df=encoded_imola_20250113_df,
    fault_col='InverterFault'
)

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

# Define output file path
output_file = os.path.join(DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, 'kmeans', 'discr-20250113-imola.csv')

# Save the discretized dataset
DataProcessor.save_dataset(kmeans_discretized_imola_20250113_df, output_file, file_format="csv")
print(f"✅ Saved discretized (KMeans) data to: {output_file}")

Dataset with shape (497553, 14), saved successfully at .\data\discretized-datasets-imola\kmeans\discr-20250113-imola.csv (csv).
✅ Saved discretized (KMeans) data to: .\data\discretized-datasets-imola\kmeans\discr-20250113-imola.csv

# Perform K-Means discretization
kmeans_discretized_imola_20250114_df, kmeans_result = DataDiscretizer.discretize_kmeans(
    df=imola_20250114_df.drop(columns=['InverterFault']), 
    n_bins=list(kmeans_bins.values())  # Extract bin values in correct order
)


# Display K-Means discretization results in a readable format
print("\n" + "="*80)
print("🔍 K-Means Discretization Summary")
print("="*80)

for col, details in kmeans_result.items():
    print(f"\n📌 Feature: {col}")
    print("-" * 60)
    print(f"  📊 Method: {details['method']}")
    print(f"  🔢 Number of Bins: {details['bin_count']}")
    print(f"  📍 Bin Edges: {details['bin_edges']}")
    print(f"  🎯 Cluster Centers: {details['cluster_centers']}")
    print(f"  📈 Value Counts: {details['value_counts']}")
    print(f"  🔄 Mapping: {details['mapping']}")
    print("-" * 60)

Processing column: BatteryVoltage_V
BatteryVoltage_V: Using provided n_bins: 3
BatteryVoltage_V discretized into 3 bins.
Processing column: BatteryCurrent_A
BatteryCurrent_A: Using provided n_bins: 3
BatteryCurrent_A discretized into 3 bins.
Processing column: BatteryPackTemp_C
BatteryPackTemp_C: Using provided n_bins: 4
BatteryPackTemp_C discretized into 4 bins.
Processing column: InverterSpeed_RearLeft_RPM
InverterSpeed_RearLeft_RPM: Using provided n_bins: 6
InverterSpeed_RearLeft_RPM discretized into 6 bins.
Processing column: Inverter_Iq_Ref_RearLeft_A
Inverter_Iq_Ref_RearLeft_A: Using provided n_bins: 3
Inverter_Iq_Ref_RearLeft_A discretized into 3 bins.
Processing column: Inverter_Id_Ref_RearLeft_A
Inverter_Id_Ref_RearLeft_A: Using provided n_bins: 6
Inverter_Id_Ref_RearLeft_A discretized into 6 bins.
Processing column: MotorTemp_RearLeft_C
MotorTemp_RearLeft_C: Using provided n_bins: 4
MotorTemp_RearLeft_C discretized into 4 bins.
Processing column: InverterTemp_RearLeft_C
InverterTemp_RearLeft_C: Using provided n_bins: 7
InverterTemp_RearLeft_C discretized into 7 bins.
Processing column: InverterSpeed_RearRight_RPM
InverterSpeed_RearRight_RPM: Using provided n_bins: 6
InverterSpeed_RearRight_RPM discretized into 6 bins.
Processing column: Inverter_Iq_Ref_RearRight_A
Inverter_Iq_Ref_RearRight_A: Using provided n_bins: 3
Inverter_Iq_Ref_RearRight_A discretized into 3 bins.
Processing column: Inverter_Id_Ref_RearRight_A
Inverter_Id_Ref_RearRight_A: Using provided n_bins: 6
Inverter_Id_Ref_RearRight_A discretized into 6 bins.
Processing column: MotorTemp_RearRight_C
MotorTemp_RearRight_C: Using provided n_bins: 4
MotorTemp_RearRight_C discretized into 4 bins.
Processing column: InverterTemp_RearRight_C
InverterTemp_RearRight_C: Using provided n_bins: 7
InverterTemp_RearRight_C discretized into 7 bins.

================================================================================
🔍 K-Means Discretization Summary
================================================================================

📌 Feature: BatteryVoltage_V
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [405.3999938964844, 465.5, 485.260009765625, 503.260009765625]
  🎯 Cluster Centers: [463.01402500742046, 487.8670697842577, 443.71776983770343]
  📈 Value Counts: {0: 205420, 1: 342734, 2: 55570}
  🔄 Mapping: {0: (405.3999938964844, 465.5), 1: (465.5, 485.260009765625), 2: (485.260009765625, 503.260009765625)}
------------------------------------------------------------

📌 Feature: BatteryCurrent_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-14.93000030517578, 0.0299999993294477, 0.0900000035762786, 116.33999633789062]
  🎯 Cluster Centers: [0.6347228325262977, 70.11582684037796, 26.173025176113846]
  📈 Value Counts: {0: 522464, 1: 17234, 2: 64026}
  🔄 Mapping: {0: (-14.93000030517578, 0.0299999993294477), 1: (0.0299999993294477, 0.0900000035762786), 2: (0.0900000035762786, 116.33999633789062)}
------------------------------------------------------------

📌 Feature: BatteryPackTemp_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [15.100000381469728, 18.950000762939453, 28.540000915527344, 30.729999542236328, 33.150001525878906]
  🎯 Cluster Centers: [16.650820800408827, 31.10757045999683, 28.16873758727166, 18.710720547811018]
  📈 Value Counts: {0: 109826, 1: 212408, 2: 221648, 3: 59842}
  🔄 Mapping: {0: (15.100000381469728, 18.950000762939453), 1: (18.950000762939453, 28.540000915527344), 2: (28.540000915527344, 30.729999542236328), 3: (30.729999542236328, 33.150001525878906)}
------------------------------------------------------------

📌 Feature: InverterSpeed_RearLeft_RPM
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-326.0, -1.0, 0.0, 3.0, 2851.0, 4808.0, 15312.0]
  🎯 Cluster Centers: [29.16145619705503, 4985.88094842793, 2209.337123656471, 9058.826691290482, 3660.076954563566, 6397.409932015105]
  📈 Value Counts: {0: 360934, 1: 72128, 2: 44864, 3: 10359, 4: 70790, 5: 44649}
  🔄 Mapping: {0: (-326.0, -1.0), 1: (-1.0, 0.0), 2: (0.0, 3.0), 3: (3.0, 2851.0), 4: (2851.0, 4808.0), 5: (4808.0, 15312.0)}
------------------------------------------------------------

📌 Feature: Inverter_Iq_Ref_RearLeft_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-7.625, 0.0, 0.0, 51.875]
  🎯 Cluster Centers: [0.4015144414907743, 39.82179805002807, 19.093881074086724]
  📈 Value Counts: {0: 493474, 1: 36103, 2: 74147}
  🔄 Mapping: {0: (-7.625, 0.0), 1: (0.0, 0.0), 2: (0.0, 51.875)}
------------------------------------------------------------

📌 Feature: Inverter_Id_Ref_RearLeft_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-52.9375, -2.0625, 0.0, 0.0, 0.0, 0.0, 0.0]
  🎯 Cluster Centers: [-0.05359657265552431, -7.8063245315163, -19.932142857142928, -2.2288847354971297, -4.615025932316691, -32.822265624999964]
  📈 Value Counts: {0: 479380, 1: 21719, 2: 980, 3: 52835, 4: 48106, 5: 704}
  🔄 Mapping: {0: (-52.9375, -2.0625), 1: (-2.0625, 0.0), 2: (0.0, 0.0), 3: (0.0, 0.0), 4: (0.0, 0.0), 5: (0.0, 0.0)}
------------------------------------------------------------

📌 Feature: MotorTemp_RearLeft_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [4.0, 27.0, 31.0, 38.0, 66.0]
  🎯 Cluster Centers: [12.245433175298668, 29.69924260576422, 41.070378301215456, 57.90890176371201]
  📈 Value Counts: {0: 76147, 1: 342226, 2: 154110, 3: 31241}
  🔄 Mapping: {0: (4.0, 27.0), 1: (27.0, 31.0), 2: (31.0, 38.0), 3: (38.0, 66.0)}
------------------------------------------------------------

📌 Feature: InverterTemp_RearLeft_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 7
  📍 Bin Edges: [0.0, 22.0, 24.0, 25.0, 27.0, 29.0, 33.0, 45.0]
  🎯 Cluster Centers: [17.068451324289217, 27.91318319685784, 22.124766433674626, 35.749308681884244, 10.768779002317565, 24.868292006721873, 31.239327123657674]
  📈 Value Counts: {0: 28166, 1: 116383, 2: 101149, 3: 74857, 4: 27584, 5: 173133, 6: 82452}
  🔄 Mapping: {0: (0.0, 22.0), 1: (22.0, 24.0), 2: (24.0, 25.0), 3: (25.0, 27.0), 4: (27.0, 29.0), 5: (29.0, 33.0), 6: (33.0, 45.0)}
------------------------------------------------------------

📌 Feature: InverterSpeed_RearRight_RPM
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-239.0, 0.0, 0.0, 0.0, 2868.0, 4727.0, 11698.0]
  🎯 Cluster Centers: [30.720205704699083, 4819.449302477875, 3531.1125359310913, 8502.343852388374, 6177.489420483198, 2079.717369619197]
  📈 Value Counts: {0: 360872, 1: 73998, 2: 71836, 3: 11483, 4: 46465, 5: 39070}
  🔄 Mapping: {0: (-239.0, 0.0), 1: (0.0, 0.0), 2: (0.0, 0.0), 3: (0.0, 2868.0), 4: (2868.0, 4727.0), 5: (4727.0, 11698.0)}
------------------------------------------------------------

📌 Feature: Inverter_Iq_Ref_RearRight_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 3
  📍 Bin Edges: [-50.375, 0.0, 0.0, 7.375]
  🎯 Cluster Centers: [-0.4094093342567513, -39.35148427334678, -18.911576733068237]
  📈 Value Counts: {0: 493676, 1: 35354, 2: 74694}
  🔄 Mapping: {0: (-50.375, 0.0), 1: (0.0, 0.0), 2: (0.0, 7.375)}
------------------------------------------------------------

📌 Feature: Inverter_Id_Ref_RearRight_A
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 6
  📍 Bin Edges: [-46.125, -2.0625, 0.0, 0.0, 0.0, 0.0, 0.0]
  🎯 Cluster Centers: [-4.588396467617704, -0.054102910237402435, -7.724731182795729, -32.82863729508191, -2.2362862332032822, -18.052535377358538]
  📈 Value Counts: {0: 46739, 1: 479102, 2: 20925, 3: 976, 4: 54922, 5: 1060}
  🔄 Mapping: {0: (-46.125, -2.0625), 1: (-2.0625, 0.0), 2: (0.0, 0.0), 3: (0.0, 0.0), 4: (0.0, 0.0), 5: (0.0, 0.0)}
------------------------------------------------------------

📌 Feature: MotorTemp_RearRight_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 4
  📍 Bin Edges: [4.0, 29.0, 34.0, 42.0, 77.0]
  🎯 Cluster Centers: [31.552076309952025, 45.704185796906636, 13.3894029183318, 67.0992184107673]
  📈 Value Counts: {0: 332224, 1: 167710, 2: 71548, 3: 32242}
  🔄 Mapping: {0: (4.0, 29.0), 1: (29.0, 34.0), 2: (34.0, 42.0), 3: (42.0, 77.0)}
------------------------------------------------------------

📌 Feature: InverterTemp_RearRight_C
------------------------------------------------------------
  📊 Method: K-Means Binning
  🔢 Number of Bins: 7
  📍 Bin Edges: [0.0, 22.0, 24.0, 25.0, 28.0, 30.0, 34.0, 46.0]
  🎯 Cluster Centers: [21.877696067917938, 36.47059690994887, 28.14853160375501, 13.040176704302757, 31.41631274235912, 24.92583922101859, 8.526512829121202e-14]
  📈 Value Counts: {0: 93284, 1: 88154, 2: 124115, 3: 38256, 4: 92063, 5: 166166, 6: 1686}
  🔄 Mapping: {0: (0.0, 22.0), 1: (22.0, 24.0), 2: (24.0, 25.0), 3: (25.0, 28.0), 4: (28.0, 30.0), 5: (30.0, 34.0), 6: (34.0, 46.0)}
------------------------------------------------------------

# Perform discretization on numeric columns using predefined bin configurations
binarization_info, binarized_fault_df = DataDiscretizer.binarize_columns(
    df=imola_20250114_df['InverterFault'].to_frame(),
    columns=['InverterFault'],
    thresholds={'InverterFault': 0.5},
    labels={'InverterFault': [0, 1]}
)

# Add the binzarized column to discretized dataframe
kmeans_discretized_imola_20250114_df['InverterFault'] = binarized_fault_df['InverterFault']

# Display summary of binarization results
print("\n📊 Binarization Overview:")
for column, info in binarization_info.items():
    print(f"\n🔹 Column: {column}")
    print(f"   - Threshold: {info['threshold']}")
    print(f"   - True (%): {info['percentage_true']}")
    print("   - Value Counts:")
    print(info['value_counts'].to_string())

📊 Binarization Overview:

🔹 Column: InverterFault
   - Threshold: 0.5
   - True (%): 12.56931975538491
   - Value Counts:
InverterFault
0    527840
1     75884

# Encode categorical columns using the specified encoding strategy
encoded_imola_20250114_df, _ = DataEncoder.encode_categorical_columns(
    df=kmeans_discretized_imola_20250114_df, 
    encoding_strategy='ordinal'
)

# Visualize the distribution of discrete variables with respect to 'InverterFault'
DataVisualizer.plot_discrete_distributions(
    df=encoded_imola_20250114_df,
    fault_col='InverterFault'
)

# Define output file path
output_file = os.path.join(DISCRETIZED_ORIGINAL_DATASETS_IMOLA_FOLDER_NAME, 'kmeans', 'discr-20250114-imola.csv')

# Save the discretized dataset
DataProcessor.save_dataset(kmeans_discretized_imola_20250114_df, output_file, file_format="csv")
print(f"✅ Saved discretized (KMeans) data to: {output_file}")

Dataset with shape (603724, 14), saved successfully at .\data\discretized-datasets-imola\kmeans\discr-20250114-imola.csv (csv).
✅ Saved discretized (KMeans) data to: .\data\discretized-datasets-imola\kmeans\discr-20250114-imola.csv

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterFault	InverterSpeed_RearLeft_RPM	Inverter_Iq_Ref_RearLeft_A	Inverter_Id_Ref_RearLeft_A	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	InverterSpeed_RearRight_RPM	Inverter_Iq_Ref_RearRight_A	Inverter_Id_Ref_RearRight_A	MotorTemp_RearRight_C	InverterTemp_RearRight_C
count	248448.000000	248448.000000	248448.000000	248448.0	248448.000000	248448.000000	248448.000000	248448.000000	248448.000000	248448.000000	248448.000000	248448.000000	248448.000000	248448.000000
mean	470.132810	9.072812	22.137357	0.0	2691.249139	6.832681	-1.531501	40.275965	29.517537	2656.734955	-6.073359	-1.456921	43.435157	29.595223
std	18.943940	19.741638	5.606731	0.0	3324.347981	12.631832	3.431754	14.733796	5.621444	3253.950091	11.886625	3.229787	16.021885	5.499356
min	413.500000	-36.689999	13.750000	0.0	-76.000000	-26.750000	-49.812500	12.000000	15.000000	-11.000000	-50.500000	-45.375000	13.000000	15.000000
25%	453.799988	0.060000	16.750000	0.0	0.000000	0.000000	-2.062500	32.000000	27.000000	0.000000	-10.812500	-2.000000	34.000000	27.000000
50%	468.540009	0.090000	21.959999	0.0	3.000000	0.000000	0.000000	39.000000	30.000000	0.000000	0.000000	0.000000	42.000000	30.000000
75%	485.420013	10.660000	27.820000	0.0	6012.000000	11.562500	0.000000	49.000000	33.000000	5861.000000	0.000000	0.000000	54.000000	33.000000
max	510.779999	115.239998	29.680000	0.0	14881.000000	52.000000	0.000000	76.000000	45.000000	12655.000000	32.875000	0.000000	80.000000	44.000000

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterFault	InverterSpeed_RearLeft_RPM	Inverter_Iq_Ref_RearLeft_A	Inverter_Id_Ref_RearLeft_A	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	InverterSpeed_RearRight_RPM	Inverter_Iq_Ref_RearRight_A	Inverter_Id_Ref_RearRight_A	MotorTemp_RearRight_C	InverterTemp_RearRight_C
count	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000	497553.000000
mean	463.542110	6.043057	15.061768	0.116209	2167.661799	6.023563	-1.143004	33.947387	25.935744	2133.151913	-5.894690	-1.118844	37.482093	26.616244
std	20.394413	14.703243	4.844621	0.320475	2369.976221	11.305793	2.497655	9.819891	5.290186	2304.850672	11.040044	2.452635	11.906781	5.114772
min	385.820007	-13.500000	5.190000	0.000000	-1439.000000	-7.062500	-52.937500	7.000000	0.000000	-573.000000	-50.500000	-45.937500	9.000000	0.000000
25%	448.380005	0.000000	11.490000	0.000000	0.000000	0.000000	-1.312500	29.000000	24.000000	0.000000	-7.562500	-1.312500	30.000000	24.000000
50%	463.579987	0.090000	15.730000	0.000000	1748.000000	0.000000	0.000000	33.000000	25.000000	1740.000000	0.000000	0.000000	36.000000	26.000000
75%	478.880005	4.360000	19.270000	0.000000	4075.000000	7.750000	0.000000	42.000000	28.000000	4023.000000	0.000000	0.000000	47.000000	29.000000
max	503.100006	115.790001	23.639999	1.000000	14967.000000	52.000000	0.000000	57.000000	45.000000	10113.000000	6.875000	0.000000	67.000000	46.000000

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterFault	InverterSpeed_RearLeft_RPM	Inverter_Iq_Ref_RearLeft_A	Inverter_Id_Ref_RearLeft_A	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	InverterSpeed_RearRight_RPM	Inverter_Iq_Ref_RearRight_A	Inverter_Id_Ref_RearRight_A	MotorTemp_RearRight_C	InverterTemp_RearRight_C
count	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000	603724.000000
mean	475.379427	5.326524	26.169941	0.125693	1833.484712	5.054590	-0.956816	31.860244	26.206788	1802.161027	-4.978979	-0.954104	35.229350	26.970097
std	16.640589	14.433446	5.712431	0.331504	2463.637554	11.118464	2.350098	11.030861	5.909046	2405.234361	10.923923	2.402517	12.961616	6.125237
min	405.399994	-14.930000	15.100000	0.000000	-326.000000	-7.625000	-52.937500	4.000000	0.000000	-239.000000	-50.375000	-46.125000	4.000000	0.000000
25%	460.880005	0.030000	18.950001	0.000000	0.000000	0.000000	-0.437500	27.000000	23.000000	0.000000	-2.062500	-0.437500	29.000000	24.000000
50%	478.940002	0.060000	28.540001	0.000000	3.000000	0.000000	0.000000	31.000000	26.000000	0.000000	0.000000	0.000000	34.000000	27.000000
75%	488.459991	0.700000	30.730000	0.000000	3873.000000	1.875000	0.000000	38.000000	30.000000	3841.000000	0.000000	0.000000	42.000000	31.000000
max	503.260010	116.339996	33.150002	1.000000	15312.000000	51.875000	0.000000	66.000000	45.000000	11698.000000	7.375000	0.000000	77.000000	46.000000

📊 Discretizing Continuous Data for the Dynamic Bayesian Networks¶

🔔 Analyzing data distributions¶

🔷 Analyzing `20241128-imola` dataset¶

🔷 Analyzing `20250113-imola` dataset¶

🔷 Analyzing `20250114-imola` dataset¶

🪓 Manual discretization with `cut` method¶

🔷 Discretizing `20241128-imola` dataset with `cut`¶

🔷 Discretizing `20250113-imola` dataset with `cut`¶

🔷 Discretizing `20250114-imola` dataset with `cut`¶

🍡 Unsupervised discretization with `KMeans` method¶

🔷 Discretizing `20241128-imola` dataset with `KMeans`¶

🔷 Discretizing `20250113-imola` dataset with `KMeans`¶

🔷 Discretizing `20250114-imola` dataset with `KMeans`¶

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	MotorTemp_RearRight_C	InverterTemp_RearRight_C
0	504.26001	0.00	13.9	13.0	15.0	14.0	15.0
1	504.26001	0.00	13.9	13.0	15.0	14.0	15.0
2	504.26001	0.00	13.9	13.0	15.0	14.0	15.0
3	504.26001	-0.03	13.9	13.0	15.0	14.0	15.0
4	504.26001	-0.03	13.9	13.0	15.0	14.0	15.0

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterSpeed_RearLeft_RPM	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	InverterSpeed_RearRight_RPM	MotorTemp_RearRight_C	InverterTemp_RearRight_C
0	502.880005	0.03	5.19	0.0	8.0	11.0	31.0	10.0	13.0
1	502.899994	0.06	5.19	0.0	8.0	11.0	31.0	10.0	13.0
2	502.899994	0.06	5.19	3.0	8.0	11.0	29.0	10.0	13.0
3	502.899994	0.06	5.19	3.0	8.0	11.0	29.0	10.0	13.0
4	502.899994	0.06	5.19	3.0	8.0	11.0	29.0	10.0	13.0

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterSpeed_RearLeft_RPM	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	MotorTemp_RearRight_C	InverterTemp_RearRight_C
0	500.220001	0.03	17.120001	0.0	9.0	10.0	10.0	10.0
1	500.220001	0.03	17.120001	0.0	9.0	10.0	10.0	10.0
2	500.220001	0.03	17.120001	-2.0	9.0	10.0	10.0	10.0
3	500.220001	0.03	17.120001	-2.0	9.0	10.0	10.0	10.0
4	500.220001	0.03	17.120001	-2.0	9.0	10.0	10.0	10.0

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterFault	InverterSpeed_RearLeft_RPM	Inverter_Iq_Ref_RearLeft_A	Inverter_Id_Ref_RearLeft_A	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	InverterSpeed_RearRight_RPM	Inverter_Iq_Ref_RearRight_A	Inverter_Id_Ref_RearRight_A	MotorTemp_RearRight_C	InverterTemp_RearRight_C
0	2_High	0_Idle	0_Low	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
1	2_High	0_Idle	0_Low	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
2	2_High	0_Idle	0_Low	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
3	2_High	0_Idle	0_Low	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
4	2_High	0_Idle	0_Low	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low

	BatteryVoltage_V	BatteryCurrent_A	BatteryPackTemp_C	InverterFault	InverterSpeed_RearLeft_RPM	Inverter_Iq_Ref_RearLeft_A	Inverter_Id_Ref_RearLeft_A	MotorTemp_RearLeft_C	InverterTemp_RearLeft_C	InverterSpeed_RearRight_RPM	Inverter_Iq_Ref_RearRight_A	Inverter_Id_Ref_RearRight_A	MotorTemp_RearRight_C	InverterTemp_RearRight_C
0	2_High	0_Idle	1_Normal	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
1	2_High	0_Idle	1_Normal	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
2	2_High	0_Idle	1_Normal	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
3	2_High	0_Idle	1_Normal	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low
4	2_High	0_Idle	1_Normal	False	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low	0_Idle	1_Close_Zero	2_Close_Zero	0_Low	0_Low

📊 Discretizing Continuous Data for the Dynamic Bayesian Networks¶

🔔 Analyzing data distributions¶

🔷 Analyzing 20241128-imola dataset¶

🔷 Analyzing 20250113-imola dataset¶

🔷 Analyzing 20250114-imola dataset¶

🪓 Manual discretization with cut method¶

🔷 Discretizing 20241128-imola dataset with cut¶

🔷 Discretizing 20250113-imola dataset with cut¶

🔷 Discretizing 20250114-imola dataset with cut¶

🍡 Unsupervised discretization with KMeans method¶

🔷 Discretizing 20241128-imola dataset with KMeans¶

🔷 Discretizing 20250113-imola dataset with KMeans¶

🔷 Discretizing 20250114-imola dataset with KMeans¶

🔷 Analyzing `20241128-imola` dataset¶

🔷 Analyzing `20250113-imola` dataset¶

🔷 Analyzing `20250114-imola` dataset¶

🪓 Manual discretization with `cut` method¶

🔷 Discretizing `20241128-imola` dataset with `cut`¶

🔷 Discretizing `20250113-imola` dataset with `cut`¶

🔷 Discretizing `20250114-imola` dataset with `cut`¶

🍡 Unsupervised discretization with `KMeans` method¶

🔷 Discretizing `20241128-imola` dataset with `KMeans`¶

🔷 Discretizing `20250113-imola` dataset with `KMeans`¶

🔷 Discretizing `20250114-imola` dataset with `KMeans`¶